AI Dynamics

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  • Understanding AI Agents: Components and Orchestration Explained

    A simple way to think about AI agents: LLM = reasoning Tools = actions Memory = context Orchestration = the loop The first three are components. The last one is what makes them an agent.

    → View original post on X — @akshay_pachaar, 2026-04-09 20:21 UTC

  • Scobleizer Refocuses on Interviewing Startup Founders with AI Assistance

    Now that my AI is reading X for me I am going to pour a lot more effort into interviewing founders again who are starting companies. You one? Drop me a line. UNALIGNED (@UnalignedX) What are some things that you would love for @Scobleizer to bring to X? Any companies that come to mind that you would love to hear about? Tag the company that you are most interested to hear about! — https://nitter.net/UnalignedX/status/2042335838848094371#m

    → View original post on X — @scobleizer, 2026-04-09 20:21 UTC

  • Brain Computer Interfaces and Robots: The Future Arrives

    Wait until brain computer interfaces and robots arrive in our lives. 🙂

    → View original post on X — @scobleizer

  • Seedance 2.0 Launch: AI Video Generation Now Available
    Seedance 2.0 Launch: AI Video Generation Now Available

    Seedance 2.0 on Runway is now available on all paid plans, including the US. All you need is an image, a video, an idea or a piece of audio to start making your interdimensional blockbuster ideas into a reality. New customers can use code SEEDANCE to get 50% off 3 months of our

    → View original post on X — @runwayml

  • Snorkel AI Reading Group Inaugural Session in San Francisco

    Introducing the Snorkel AI Reading Group, where we’re diving into papers shaping frontier AI with their authors. Join our inaugural session in San Francisco on April 29th (RSVP): https://
    luma.com/da3tj3pr @MayeeChen with @StanfordAILab and @hazyresearch will dive into her paper

    → View original post on X — @snorkelai

  • AI Capability Gap: Free Models vs Frontier Agentic Systems

    Judging by my tl there is a growing gap in understanding of AI capability. The first issue I think is around recency and tier of use. I think a lot of people tried the free tier of ChatGPT somewhere last year and allowed it to inform their views on AI a little too much. This is a group of reactions laughing at various quirks of the models, hallucinations, etc. Yes I also saw the viral videos of OpenAI's Advanced Voice mode fumbling simple queries like "should I drive or walk to the carwash". The thing is that these free and old/deprecated models don't reflect the capability in the latest round of state of the art agentic models of this year, especially OpenAI Codex and Claude Code. But that brings me to the second issue. Even if people paid $200/month to use the state of the art models, a lot of the capabilities are relatively "peaky" in highly technical areas. Typical queries around search, writing, advice, etc. are *not* the domain that has made the most noticeable and dramatic strides in capability. Partly, this is due to the technical details of reinforcement learning and its use of verifiable rewards. But partly, it's also because these use cases are not sufficiently prioritized by the companies in their hillclimbing because they don't lead to as much $$$ value. The goldmines are elsewhere, and the focus comes along. So that brings me to the second group of people, who *both* 1) pay for and use the state of the art frontier agentic models (OpenAI Codex / Claude Code) and 2) do so professionally in technical domains like programming, math and research. This group of people is subject to the highest amount of "AI Psychosis" because the recent improvements in these domains as of this year have been nothing short of staggering. When you hand a computer terminal to one of these models, you can now watch them melt programming problems that you'd normally expect to take days/weeks of work. It's this second group of people that assigns a much greater gravity to the capabilities, their slope, and various cyber-related repercussions. TLDR the people in these two groups are speaking past each other. It really is simultaneously the case that OpenAI's free and I think slightly orphaned (?) "Advanced Voice Mode" will fumble the dumbest questions in your Instagram's reels and *at the same time*, OpenAI's highest-tier and paid Codex model will go off for 1 hour to coherently restructure an entire code base, or find and exploit vulnerabilities in computer systems. This part really works and has made dramatic strides because 2 properties: 1) these domains offer explicit reward functions that are verifiable meaning they are easily amenable to reinforcement learning training (e.g. unit tests passed yes or no, in contrast to writing, which is much harder to explicitly judge), but also 2) they are a lot more valuable in b2b settings, meaning that the biggest fraction of the team is focused on improving them. So here we are. staysaasy (@staysaasy) The degree to which you are awed by AI is perfectly correlated with how much you use AI to code. — https://nitter.net/staysaasy/status/2042063369432183238#m

    → View original post on X — @karpathy, 2026-04-09 20:10 UTC

  • Pokee AI Ideathon: From Ideas to Impact with AWS
    Pokee AI Ideathon: From Ideas to Impact with AWS

    We're excited to announce the Pokee AI Ideathon: From Ideas to Impact with AWS Build! This is an in-person event for San Francisco / Bay Area founders, builders, creatives, and strategists to come together and turn bold ideas into compelling concepts, prototypes, and products in just a few hours. More than a traditional hackathon, this Ideathon is designed around a broader question: How do you quickly move from insight to workflow, execution and impact with AI? [Translated from EN to English]

    → View original post on X — @scobleizer, 2026-04-09 20:09 UTC

  • Iceberg v3 Public Preview: Unifying Data Layer Across Engines
    Iceberg v3 Public Preview: Unifying Data Layer Across Engines

    Iceberg v3 marks a major step forward for open table formats in unifying the data layer. With v3 now in Public Preview on Databricks, it enables: • High-performance incremental data processing • Native support for semi-structured data • Interoperability across engines and

    → View original post on X — @databricks

  • NVIDIA Jetson Enables Open Source Robotics Autonomy Deployment

    Across NVIDIA Jetson and our robotics software stack, we’re focused on making it easy for developers to turn open source innovation, like @openclaw
    , into deployable, real‑world autonomy on the edge.

    → View original post on X — @nvidia

  • Global Robotics Community Directory Worldwide

    More accurately this is everyone I can find in robotics world wide. 🙂 Not just Bangalore.

    → View original post on X — @scobleizer